Market Trends
Stephanie Langeland
- 1 File Info
- 2 Open and Close Data
- 2.1 U.S. corporate credit spread (BICLB10Y) - last price only
- 2.2 10-year constant maturity rate (H15T10Y)
- 2.3 S&P 500 Index (SPX Index)
- 2.4 3-month U.S. overnight index swap rate (USSOC Currency)
- 2.5 CBOE VIX Index (VIX)
- 2.6 Crude oil price (CLA Comdty)
- 2.7 Moody’s BAA corporate bond yield (MOODCBAA Index) - last price only
- 2.8 CBOE Interest Rate 10-year Treasury Note (^TNX)
- 3 Intraday Data
- 4 Conclusion: Only use open vs. close data in the final LDA model.
1 File Info
Date: 04-18-2017
Author: Stephanie Langeland
File Name: 10_market_trends_20180418_v1.Rmd
Version: 78
Previous Version/File: 10_market_trends_20180411_v1.Rmd
Dependencies: None
Purpose:
Explore open vs. close data and intraday data on FOMC and non-FOMC dates.
Decide whether to use open vs. close and/or intraday data as response variables in the final LDA model.
Input File(s):
open_close_data.rdscla_intraday.rdsct10govt_intraday.rdsspx_intraday.rdsussoc_intraday.rdsvix_intraday.rds
Output File(s): /code/market_trends_images/
Data Output: None
Required by: QMSS Master’s Thesis
Status: Complete
Machine: 2018 MacBook Pro
R version: R version 3.4.3 (2017-11-30), Kite-Eating Tree
2 Open and Close Data
## :::::::::::::::::::: Import Open and Close Data:
options(scipen = 999)
oc_data <- readRDS("/Users/stephanielangeland/Desktop/Columbia/masters_thesis/Langeland_thesis/input_output_files/open_close_data.rds")
str(oc_data)
##View(oc_data)
## :::::::::::::::::::: Add Recession Dates:
oc_data$recession_date <- ifelse(
oc_data$release_date > "2001-03-01" &
oc_data$release_date < "2001-11-01" ,
Inf,
0
)
oc_data$recession_date <- ifelse(
oc_data$release_date > "2007-12-01" &
oc_data$release_date < "2009-06-01" ,
Inf,
oc_data$recession_date
)
## :::::::::::::::::::: Create subsets of the dataset - FOMC release dates vs. non-release dates
library(dplyr)
releases_sub <- oc_data %>%
filter(
!is.na(stmt_text) |
!is.na(min_text)
)
no_releases_sub <- oc_data %>%
filter(
is.na(stmt_text) &
is.na(min_text)
)2.1 U.S. corporate credit spread (BICLB10Y) - last price only
## :::::::::::::::::::: Plot:
library(ggplot2)
spread_plot <- ggplot() +
geom_area(aes(x = oc_data$release_date,
y = oc_data$recession_date,
alpha = 0.05),
fill = "gray83",
show.legend = F) +
geom_point(aes(x = no_releases_sub$release_date,
y = no_releases_sub$BICLB10Y_LAST_PRICE,
color = "Non-FOMC Dates"
)) +
geom_point(aes(x = no_releases_sub$release_date,
y = no_releases_sub$BICLB10Y_LAST_PRICE,
color = "Non-FOMC Dates",
text = paste("Non-FOMC Date: ",
as.character(no_releases_sub$release_date),
"\n",
"U.S. corporate credit spread (last price): ",
no_releases_sub$BICLB10Y_LAST_PRICE)),
size = 0.9) +
geom_point(aes(x = releases_sub$release_date,
y = releases_sub$BICLB10Y_LAST_PRICE,
color = "FOMC Dates",
text = paste("FOMC Meeting Materials Release Date: ",
as.character(releases_sub$release_date),
"\n",
"U.S. corporate credit spread (last price): ",
releases_sub$BICLB10Y_LAST_PRICE))) +
labs(title = "U.S. Corporate Credit Spread on FOMC vs. Non-FOMC Dates",
x = "Date",
y = "U.S. Corporate Credit Spread") +
scale_colour_manual("Legend",
breaks = c("FOMC Dates",
"Non-FOMC Dates"),
values = c("magenta1", "gray45")) +
theme(
panel.border = element_rect(fill = NA),
panel.background = element_rect(fill = "white"),
panel.grid.major = element_line(colour = "gray",
size = 0.25),
panel.grid.minor = element_line(colour = "gray",
size = 0.25)
)
spread_plot
Interactive Plot:
library(plotly)
ggplotly(spread_plot,
tooltip = c("text"))## :::::::::::::::::::: Tables:
mean(releases_sub$BICLB10Y_LAST_PRICE,
na.rm = T) ## mean on FOMC dates## [1] 257.6886mean(no_releases_sub$BICLB10Y_LAST_PRICE,
na.rm = T) ## mean on non-FOMC dates## [1] 248.6605Notes: Average U.S. corporate credit spread is higher on release days.
2.2 10-year constant maturity rate (H15T10Y)
library(QMSS)
Tab(releases_sub$H15T10Y_change_cat) ## FOMC dates## Count Pct Cum.Pct
## -1 130 36.93 36.93
## 0 124 35.23 72.16
## 1 98 27.84 100.00Tab(no_releases_sub$H15T10Y_change_cat) ## non-FOMC dates## Count Pct Cum.Pct
## -1 2060 36.19 36.19
## 0 1981 34.80 70.99
## 1 1651 29.01 100.00Notes: Doesn’t seem like FOMC releases change the trend at all.
2.3 S&P 500 Index (SPX Index)
Tab(releases_sub$SPX_change_cat) ## FOMC dates## Count Pct Cum.Pct
## -1 159 45.17 45.17
## 1 193 54.83 100.00Tab(no_releases_sub$SPX_change_cat) ## non-FOMC dates## Count Pct Cum.Pct
## -1 2630 46.21 46.21
## 0 5 0.09 46.29
## 1 3057 53.71 100.00Notes: Doesn’t seem like FOMC releases change the trend at all.
2.4 3-month U.S. overnight index swap rate (USSOC Currency)
Tab(releases_sub$USSOC_change_cat) ## FOMC dates## Count Pct Cum.Pct
## -1 110 41.20 41.20
## 0 15 5.62 46.82
## 1 142 53.18 100.00Tab(no_releases_sub$USSOC_change_cat) ## non-FOMC dates## Count Pct Cum.Pct
## -1 1601 40.32 40.32
## 0 227 5.72 46.03
## 1 2143 53.97 100.00Notes: Doesn’t seem like FOMC releases change the trend at all.
2.5 CBOE VIX Index (VIX)
Tab(releases_sub$VIX_change_cat) ## FOMC dates## Count Pct Cum.Pct
## -1 229 65.06 65.06
## 0 2 0.57 65.62
## 1 121 34.38 100.00Tab(no_releases_sub$VIX_change_cat) ## non-FOMC dates## Count Pct Cum.Pct
## -1 3227 56.69 56.69
## 0 36 0.63 57.33
## 1 2429 42.67 100.00Notes: Doesn’t seem like FOMC releases change the trend at all.
2.6 Crude oil price (CLA Comdty)
Tab(releases_sub$CLAComdty_change_cat) ## FOMC dates## Count Pct Cum.Pct
## -1 20 55.56 55.56
## 1 16 44.44 100.00Tab(no_releases_sub$CLAComdty_change_cat) ## non-FOMC dates## Count Pct Cum.Pct
## -1 263 47.64 47.64
## 0 3 0.54 48.19
## 1 286 51.81 100.00Notes:
Never 0 (no volatility in the 3-month U.S. overnight index swap rate) on FOMC dates
3-month U.S. overnight index swap rate usually goes down on FOMC dates so the fed might calm markets - could indicate successful transparency because they don’t disrupt the market too much. The opposite is true overall on non-FOMC dates.
2.7 Moody’s BAA corporate bond yield (MOODCBAA Index) - last price only
mean(releases_sub$MOODCBAA_LAST_PRICE, na.rm = T) ## FOMC dates## [1] 6.454063mean(no_releases_sub$MOODCBAA_LAST_PRICE, na.rm = T) ## non-FOMC dates## [1] 6.554984Notes: Doesn’t seem like FOMC releases change the trend at all.
2.8 CBOE Interest Rate 10-year Treasury Note (^TNX)
Tab(releases_sub$TNX_change_cat) ## FOMC dates## Count Pct Cum.Pct
## -1 163 46.31 46.31
## 0 12 3.41 49.72
## 1 177 50.28 100.00Tab(no_releases_sub$TNX_change_cat) ## non-FOMC dates## Count Pct Cum.Pct
## -1 2753 50.38 50.38
## 0 173 3.17 53.55
## 1 2538 46.45 100.00Notes: T-bill rate usually goes up on FOMC dates but it usually go down overall on non-FOMC dates. Historically, they’ve lowered rates more than they’ve lowered them during the sample period:
Tab(oc_data$TNX_change_cat)## Count Pct Cum.Pct
## -1 2916 50.14 50.14
## 0 185 3.18 53.32
## 1 2715 46.68 100.003 Intraday Data
3.1 S&P 500 Index (SPX Index)
spx <- readRDS("/Users/stephanielangeland/Desktop/Columbia/masters_thesis/Langeland_thesis/input_output_files/spx_intraday.rds")
str(spx)
releases_spx_sub <- spx %>%
filter(
!is.na(type) |
!is.na(type)
)
releases_spx_sub <- na.omit(releases_spx_sub)
no_releases_spx_sub <- spx %>%
filter(
is.na(type) &
is.na(type)
)spx_rel <- colMeans(releases_spx_sub[, 5:62],
na.rm = T) ## fomc dates
#spx_rel
spx_norel <- colMeans(no_releases_spx_sub[, 5:62],
na.rm = T) ## non-fomc dates
#spx_norel plot(spx_rel,
type = "p",
col = "red",
ylab = "")
par(new = TRUE)
plot(spx_norel,
type = "l",
col = "black")
Notes: Doesn’t look like there’s a difference in the 2 trends.
3.2 3-month U.S. overnight index swap rate (USSOC Currency)
ussoc <- readRDS("/Users/stephanielangeland/Desktop/Columbia/masters_thesis/Langeland_thesis/input_output_files/ussoc_intraday.rds")
str(ussoc)
#View(ussoc)
releases_ussoc_sub <- ussoc %>%
filter(
!is.na(type) |
!is.na(type)
)
releases_ussoc_sub <- na.omit(releases_ussoc_sub)
##View(releases_ussoc_sub)
no_releases_ussoc_sub <- ussoc %>%
filter(
is.na(type) &
is.na(type)
)
#View(no_releases_ussoc_sub)ussoc_rel <- colMeans(releases_ussoc_sub[, 5:90],
na.rm = T) ## fomc dates
#ussoc_rel
ussoc_norel <-colMeans(no_releases_ussoc_sub[, 5:90],
na.rm = T) ## non-fomc dates
#ussoc_norelplot(ussoc_rel,
type = "p",
col = "red",
ylab = "")
par(new = TRUE)
plot(ussoc_norel,
type = "l",
col = "black")
Notes: A little bit of a difference in trend.
3.3 CBOE VIX Index (VIX)
vix <- readRDS("/Users/stephanielangeland/Desktop/Columbia/masters_thesis/Langeland_thesis/input_output_files/vix_intraday.rds")
str(vix)
#View(vix)
releases_vix_sub <- vix %>%
filter(
!is.na(type) |
!is.na(type)
)
releases_vix_sub <- na.omit(releases_vix_sub)
#View(releases_vix_sub)
no_releases_vix_sub <- vix %>%
filter(
is.na(type) &
is.na(type)
)
#View(no_releases_vix_sub)vix_rel <- colMeans(releases_vix_sub[, 5:34],
na.rm = T) ## fomc dates
#vix_rel
vix_norel <- colMeans(no_releases_vix_sub[, 5:34],
na.rm = T) ## non-fomc dates
#vix_norelplot(vix_rel,
type = "p",
col = "red",
ylab = "")
par(new = TRUE)
plot(vix_norel,
type = "l",
col = "black")
Notes: Little bit of a difference in trend - release dates are trending lower.
3.4 Crude oil price (CLA Comdty)
cla <- readRDS("/Users/stephanielangeland/Desktop/Columbia/masters_thesis/Langeland_thesis/input_output_files/cla_intraday.rds")
str(cla)
#View(cla)
releases_cla_sub <- cla %>%
filter(
!is.na(type) |
!is.na(type)
)
releases_cla_sub <- na.omit(releases_cla_sub)
#View(releases_cla_sub)
no_releases_cla_sub <- cla %>%
filter(
is.na(type) &
is.na(type)
)
#View(no_releases_cla_sub)cla_rel <- colMeans(releases_cla_sub[, 5:41],
na.rm = T) ## fomc dates
#cla_rel
cla_norel <- colMeans(no_releases_cla_sub[, 5:41],
na.rm = T) ## non-fomc dates
#cla_norelplot(cla_rel,
type = "p",
col = "red",
ylab = "")
par(new = TRUE)
plot(cla_norel,
type = "l",
col = "black")
Notes: Little bit of a difference in trend - release dates are trending higher.
3.5 10-year Treasury yield (CT10 Govt)
ct10 <- readRDS("/Users/stephanielangeland/Desktop/Columbia/masters_thesis/Langeland_thesis/input_output_files/ct10govt_intraday.rds")
str(ct10)
#View(ct10)
releases_ct10_sub <- ct10 %>%
filter(
!is.na(type) |
!is.na(type)
)
releases_ct10_sub <- na.omit(releases_ct10_sub)
#View(releases_ct10_sub)
no_releases_ct10_sub <- ct10 %>%
filter(
is.na(type) &
is.na(type)
)
#View(no_releases_ct10_sub)ct10_rel <- colMeans(releases_ct10_sub[, 5:90],
na.rm = T) ## fomc dates
#ct10_rel
ct10_norel <- colMeans(no_releases_ct10_sub[, 5:90],
na.rm = T) ## non-fomc dates
#ct10_norelplot(ct10_rel,
type = "p",
col = "red",
ylab = "")
par(new = TRUE)
plot(ct10_norel,
type = "l",
col = "black")
Notes: Doesn’t look like there’s a difference in the 2 trends.